Library "regressions" This library computes least square regression models for polynomials of any form for a given data set of x and y values. fit(X, y, reg_type, degrees) Takes a list of X and y values and the degrees of the polynomial and returns a least square regression for the given polynomial on the dataset. Parameters: X (array) : (float ) ...
Library "KernelFunctionsFilters" This library provides filters for non-repainting kernel functions for Nadaraya-Watson estimator implementations made by @jdehorty. Filters include a smoothing formula and zero lag formula. You can find examples in the code. For more information check out the original library KernelFunctions. rationalQuadratic(_src, _lookback,...
Library "KernelFunctions" This library provides non-repainting kernel functions for Nadaraya-Watson estimator implementations. This allows for easy substitution/comparison of different kernel functions for one another in indicators. Furthermore, kernels can easily be combined with other kernels to create newer, more customized kernels. Compared to Moving...
Library "curve" Regression array Creator. Handy for weights, Auto Normalizes array while holding curves. curve(_size, _power) Curve Regression Values Tool Parameters: _size : (float) Number of Steps required (float works, future consideration) _power : (float) Strength of value decrease Returns: (float ) Array of multipliers from 1 downwards to 0.
Library "FunctionPolynomialFit" Performs Polynomial Regression fit to data. In statistics, polynomial regression is a form of regression analysis in which the relationship between the independent variable x and the dependent variable y is modelled as an nth degree polynomial in x. reference: en.wikipedia.org www.bragitoff.com gauss_elimination(A, m, n) ...
Library "regress" produces the slope (beta), y-intercept (alpha) and coefficient of determination for a linear regression regress(x, y, len) regress: computes alpha, beta, and r^2 for a linear regression of y on x Parameters: x : the explaining (independent) variable y : the dependent variable len : use the most recent "len" values of x and...
Library "FunctionPolynomialRegression" TODO: polyreg(sample_x, sample_y) Method to return a polynomial regression channel using (X,Y) sample points. Parameters: sample_x : float array, sample data X points. sample_y : float array, sample data Y points. Returns: tuple with: _predictions: Array with adjusted Y values. _max_dev: Max...